code: def forward(self, x):
      if x.ndim - 1 < self.dim:
          raise ValueError(
              f"Expect x to have {self.dim + 1} dimensions, but got {x.ndim}"
          )
      mu_ = x.mean(dim=self.dim, keepdim=True)
      std_ = torch.sqrt(
          x.var(dim=self.dim, unbiased=False, keepdim=True) + self.eps
      )
      x_hat = ((x - mu_) / std_) * self.gamma + self.beta
      return x_hat
  
imports: import warnings
  import torch
  import torch.nn
  from espnet2.enh.layers.dptnet import ImprovedTransformerLayer
  from espnet2.enh.layers.tcn import ChannelwiseLayerNorm
  from espnet2.torch_utils.get_layer_from_string import get_layer
label_desc: Use image node_id 2 for calling the LayerNormalization obj's underlying member method code with example usage: obj.forward(x) and returns: x_hat